This document demonstrates the use of the bRF and LASSO-D3S functions for integrative GRN inference.
Those functions infer the regulatory pathways of Arabidopsis thaliana’s roots in response to nitrate (N) induction from Varala et al., 2018.
They use as inputs the expression profiles of N-responsive genes and TFBS information. Prior TFBS information was built by searching in the promoters of the N-responsive genes the PWM of the N-responsive regulators.
Import of the expression data and the N-responsive genes and regulators :
load('rdata/inference_input_N_response_varala.rdata')
genes <- input_data$grouped_genes; length(genes)
## [1] 1426
tfs <- input_data$grouped_regressors; length(tfs)
## [1] 201
counts <- input_data$counts; dim(counts)
## [1] 1426 45
load("rdata/pwm_occurrences_N_response_varala.rdata")
dim(pwm_occurrence)
## [1] 1426 201
ALPHAS <- seq(0,1, by = 0.1)
# subset <- sample(genes, replace = F, size = 20)
subset <- genes
# lmses <- data.frame(row.names = subset)
# N <-100
# for(alpha in ALPHAS){
# for(perm in 1:N){
# lmses[,paste(as.character(alpha), perm, "true_data")] <- bRF_inference_MSE(counts, subset, tfs, alpha = alpha, nTrees = 2000,
# pwm_occurrence = pwm_occurrence, nCores = 45, tf_expression_permutation = F)
# }
#
# for(perm in 1:N){
# lmses[,paste(as.character(alpha), perm, "shuffled")] <- bRF_inference_MSE(counts, subset, tfs, alpha = alpha, nTrees = 2000,
# pwm_occurrence = pwm_occurrence, nCores = 45, tf_expression_permutation = T)
# }
# }
#
# save(lmses, file = "results/brf_perumtations_mse_all_genes.rdata")
load("results/brf_perumtations_mse_all_genes.rdata")
# subset<-unique(rownames(lmses))
draw_gene <- function(gene){
lmses[gene,] %>%
gather() %>%
separate(key, into = c("alpha", "rep", "MSEtype"), sep = " ") %>%
ggplot(aes(x=alpha, y=value, group =interaction(rep, MSEtype),
color = MSEtype))+ggtitle(gene)+ylab("MSE/Var(gene)")+
xlab("alpha")+
geom_line()+ggtitle(gene)+theme_pubr(legend = "top")
}
draw_gene_mean_sd <- function(gene, title = NULL){
data <- lmses[gene, ] %>%
gather() %>%
separate(key,
into = c("alpha", "rep", "MSEtype"),
sep = " ") %>%
group_by(alpha, MSEtype) %>%
mutate(mean_mse = mean(value, na.rm = T),
sd_mse = sd(value, na.rm = T)) %>%
ggplot(aes(
x = as.numeric(alpha),
y = value,
color = MSEtype,
fill = MSEtype
)) +ggtitle(paste(gene, title))+ylab("MSE/Var(gene)")+
geom_ribbon(aes(ymin = mean_mse - sd_mse ,
ymax = mean_mse + sd_mse ),
alpha = .4) +theme_pubr(legend = "top")+
geom_point(alpha = 0.1) + geom_smooth(se=F)+xlab("alpha")
}
get_diff_curves <- function(lmses){
data <- lmses %>%
rownames_to_column('gene') %>%
reshape2::melt()%>%
separate(variable,
into = c("alpha", "rep", "MSEtype"),
sep = " ") %>%
group_by(gene, alpha, MSEtype) %>%
mutate(mean_mse = mean(value, na.rm = T),
sd_mse = sd(value, na.rm = T)) %>%
dplyr::select(mean_mse, sd_mse, gene, alpha, MSEtype)%>%
distinct()
data_true <- filter(data, MSEtype=="true_data")
data_perm <- filter(data, MSEtype=="shuffled")
data_true$mean_mse_perm <- data_perm$mean_mse
data_true$sd_mse_perm <- data_perm$sd_mse
return(data_true %>%
mutate(mean_mse_diff = (mean_mse-mean_mse_perm)/sd_mse_perm))
# %>%
# ggplot(aes(x=as.numeric(alpha), y=mean_mse_diff, color = gene))+
# geom_line()
}
# for(gene in sample(genes,50, replace = F)){
# print(draw_gene(gene)+draw_gene_mean_sd(gene))
# }
lmses <- data.frame(row.names = subset)
N<-100
for(alpha in ALPHAS){
# set.seed(121314)
for(perm in 1:N){
lmses[,paste(as.character(alpha), perm, "true_data")] <- LASSO.D3S_inference_MSE(counts, subset, tfs, alpha = alpha, N=100,
pwm_occurrence = pwm_occurrence, nCores = 40, tf_expression_permutation = F)
lmses[,paste(as.character(alpha), perm, "shuffled")] <- LASSO.D3S_inference_MSE(counts, subset, tfs, alpha = alpha, N=100,
pwm_occurrence = pwm_occurrence, nCores = 40, tf_expression_permutation = T)
}
}
save(lmses, file = "results/lasso_perumtations_mse_all_genes.rdata")
# load("results/lasso_perumtations_mse_all_genes.rdata")
# for(gene in subset){
# print(draw_gene(gene) +draw_gene_mean_sd(gene))
# }
Based on the difference curves between true and permuted data
diffs <- get_diff_curves(lmses)
diffs %>%
ggplot(aes(x=as.numeric(alpha), y=mean_mse_diff, group = gene))+
geom_line(alpha = 0.2)
fractions_out <- diffs %>%
mutate(diff_greater_than_sd = ifelse(abs(mean_mse_diff)>1, 1, 0)) %>%
group_by(gene) %>%
summarise(fraction_out = sum(diff_greater_than_sd)/11);fractions_out<-
setNames(fractions_out$fraction_out, fractions_out$gene)
diff_curves <- diffs[c("gene", "alpha", "mean_mse_diff")] %>%
spread(alpha, mean_mse_diff) %>%
column_to_rownames("gene") %>%
as.matrix()
diff_curves<-diff_curves[fractions_out[rownames(diff_curves)] > 0,]
cor_clust = function(x) hclust(as.dist(1-cor(t(x))), method = "average")
Heatmap(diff_curves, cluster_rows = cor_clust,
cluster_columns = F, show_row_names = F)
clusters_rf <- cutree(cor_clust(diff_curves), k = 2)
table(clusters_rf)
## clusters_rf
## 1 2
## 346 325
clusters_rf<- c(clusters_rf,setNames(rep("no diff", sum(fractions_out==0)),
names(fractions_out[fractions_out==0])))
table(clusters_rf)
## clusters_rf
## 1 2 no diff
## 346 325 755
save(clusters_rf, file = "results/clusters_mse_bRF_100permutations.rdata")
for(gene in sample(genes,40, replace = F)){
print(draw_gene(gene)+
draw_gene_mean_sd(gene, title = paste(clusters_rf[gene], round(fractions_out[gene], 4))))
}
ha = HeatmapAnnotation(
alpha = anno_simple(as.numeric(rep(colnames(diff_curves),1))),
annotation_name_side = "left")
# draw a heatmap of the genes mean_mse on real data
true_mse <- diffs[c("gene", "alpha", "mean_mse")] %>%
spread(alpha, mean_mse) %>%
column_to_rownames("gene") %>%
as.matrix()
true_mse_pos <- true_mse[names(clusters_rf[clusters_rf==2]),]
Heatmap((true_mse-rowMeans(true_mse))/matrixStats::rowSds(true_mse),
cluster_columns = F, show_row_names = F, top_annotation = ha)+
rowAnnotation(
clusters_rf = clusters_rf[rownames(true_mse)],
col=list(clusters_rf= setNames(c("darkorange", "darkgreen", "lightgrey"),
nm = names(table(clusters_rf)))))
Heatmap((true_mse_pos-rowMeans(true_mse_pos))/matrixStats::rowSds(true_mse_pos),
cluster_columns = F, show_row_names = F, top_annotation = ha)+
rowAnnotation(
clusters_rf = clusters_rf[rownames(true_mse_pos)],
col=list(clusters_rf= setNames(c("darkorange", "darkgreen", "lightgrey"),
nm = names(table(clusters_rf)))))
load("rdata/pwm_prom_jaspar_dap.rdata")
load("rdata/gene_structure.rdata")
mean_expr <- rowMeans(counts)[genes]
var_expr <- matrixStats::rowSds(counts[genes,])*matrixStats::rowSds(counts[genes,])
pwm_prom_n_TFs <- pwm_prom[pwm_prom$TF %in% tfs,]
library(patchwork)
# to comment for new version where mse is already normalized per genes
# norm_mse <- exp(as.matrix(cbind(lmses, lmses_lasso)))/var_expr
genes_info <- data.frame(genes = genes,
cluster_rf = clusters_rf[genes])
genes_info$is_tf <- genes %in% tfs
genes_info$var <- var_expr
genes_info$expr <- mean_expr
genes_info$min_mse <- matrixStats::rowMins(as.matrix(true_mse))
genes_info$nb_motifs <- table(pwm_prom$target)[genes]
genes_info$nb_motifs_n_tfs <- table(pwm_prom_n_TFs$target)[genes]
genes_info[,c("n_introns", "n_transcripts")] <-
gene_structure[match(genes_info$gene, gene_structure$gene),
c("n_introns", "n_transcripts")]
genes_info%>%
ggplot(aes(x=cluster_rf, y=log(n_introns))) +
geom_violin(fill="darkblue", alpha=0.2)+geom_jitter(width=0.1)+
geom_boxplot(width=0.1, fill = "white")+
ggtitle(("Number of introns for RF groups")) +
genes_info%>%
ggplot(aes(x=cluster_rf, y=n_transcripts)) +
geom_boxplot(width=0.1, fill = "white")+
geom_violin(fill="darkblue", alpha=0.2)+
ggtitle(("Number of transcripts for RF groups")) +
stat_compare_means()
genes_info%>%
ggplot(aes(x=cluster_rf, y=nb_motifs)) +
geom_violin(fill="darkblue", alpha=0.2)+geom_jitter(width=0.1)+
geom_boxplot(width=0.1, fill = "white")+
ggtitle(("Number of motifs in promoter for RF groups")) +
genes_info%>%
ggplot(aes(x=cluster_rf, y=nb_motifs_n_tfs)) +
geom_violin(fill="darkblue", alpha=0.2)+geom_jitter(width=0.1)+
geom_boxplot(width=0.1, fill = "white")+
ggtitle(("Number of motifs of N-responsive TFs in promoter for RF groups")) +
stat_compare_means() + genes_info%>%
ggplot(aes(x=cluster_rf, y=min_mse)) +
geom_violin(fill="darkblue", alpha=0.2)+geom_jitter(width=0.1)+
geom_boxplot(width=0.1, fill = "white")+
ggtitle(("Min mse for RF groups")) +
stat_compare_means()
genes_info%>%
ggplot(aes(x=cluster_rf, y=var)) +
geom_violin(fill="darkblue", alpha=0.2)+geom_jitter(width=0.1)+
geom_boxplot(width=0.1, fill = "white")+
ggtitle(("Gene variance for RF groups")) + scale_y_log10()+
stat_compare_means()+ genes_info%>%
ggplot(aes(x=cluster_rf, y=expr)) +
geom_violin(fill="darkblue", alpha=0.2)+geom_jitter(width=0.1)+
geom_boxplot(width=0.1, fill = "white")+
ggtitle(("Gene expression for RF groups")) + scale_y_log10()+
stat_compare_means()
genes_info %>%
group_by(cluster_rf) %>%
summarise(n=n(),
tf_frac=sum(is_tf)/n()) %>%
ggplot(aes(x=cluster_rf, y=tf_frac,
label = paste("n=",n))) +
geom_bar(stat = "identity", aes(fill=tf_frac), alpha = 1)+
geom_hline(yintercept = length(tfs)/length(genes)) +
geom_text(y=0.2) + xlab("cluster RF") +
ggtitle("Fraction of TFs in RF groups")+ylim(c(0,0.2))
# promoteurs enrichis en certains motifs de TFs?
known_tfs <- tfs[which(tfs %in% pwm_prom$TF)]
get_number_of_motifs_per_tfs <- function(genes){
table(pwm_prom[pwm_prom$target %in% genes & pwm_prom$TF %in% tfs,"TF"])[known_tfs]
}
targets_per_pwm <- data.frame(row.names = known_tfs)
for(group in unique(clusters_rf)){
# targets_per_pwm[paste("lasso", group)] <- get_number_of_motifs_per_tfs(names(
# which(clusters_lasso == group)))/sum(clusters_lasso == group)
targets_per_pwm[paste("rf", group)] <- get_number_of_motifs_per_tfs(names(
which(clusters_rf == group)))/sum(clusters_rf == group)
}
enrichments_per_pwm <- data.frame(row.names = known_tfs)
n_genes <- length(genes)
for(group in unique(clusters_rf)){
# number of motifs in all the genes
n_targets_lasso_in_all <- get_number_of_motifs_per_tfs(genes)
# n_targets_lasso_in_group <- get_number_of_motifs_per_tfs(names(
# which(clusters_lasso == group)))
# n_group_lasso <- length(names(which(clusters_lasso == group)))
n_targets_rf_in_group <- get_number_of_motifs_per_tfs(names(
which(clusters_rf == group)))
n_group_rf <- length(names(which(clusters_rf == group)))
for(tf in known_tfs){
# number of genes with that motif in all genes
n_targets_in_all_tf <- n_targets_lasso_in_all[tf]
# number of genes with that motif in the lasso group
# n_targets_lasso_in_group_tf <- n_targets_lasso_in_group[tf]
# p_lasso <- phyper(q=n_targets_lasso_in_group_tf-1,
# m=n_targets_in_all_tf, #white balls
# n=n_genes-n_targets_in_all_tf, # black balls
# k=n_group_lasso, lower.tail = FALSE)
# number of genes with that motif in the rf group
n_targets_rf_in_group_tf <- n_targets_rf_in_group[tf]
p_rf <- phyper(q=n_targets_rf_in_group_tf-1,
m=n_targets_in_all_tf,
n=n_genes-n_targets_in_all_tf,
k=n_group_rf, lower.tail = FALSE)
# enrichments_per_pwm[tf, paste0(group, "lasso")]<- p_lasso
enrichments_per_pwm[tf, paste0(group, "rf")]<- p_rf
}
}
enrichments_per_pwm[enrichments_per_pwm<0.05] <- 0
enrichments_per_pwm[enrichments_per_pwm>=0.05] <- 1
Heatmap(enrichments_per_pwm, cluster_columns = F)
tfs_rf_pwm_pos <- rownames(enrichments_per_pwm[enrichments_per_pwm$`2rf`==0,])
# tfs_lasso_pwm_pos <- rownames(enrichments_per_pwm[enrichments_per_pwm$`0.7-1lasso`==0,])
DIANE::get_gene_information(tfs_rf_pwm_pos, organism = "Arabidopsis thaliana")
# DIANE::get_gene_information(tfs_lasso_pwm_pos, organism = "Arabidopsis thaliana")
# DIANE::get_gene_information(intersect(tfs_rf_pwm_pos, tfs_lasso_pwm_pos ), organism = "Arabidopsis thaliana")
tfs_rf_pwm_bad <- rownames(enrichments_per_pwm[enrichments_per_pwm$`1rf`==0,])
# tfs_lasso_pwm_bad <- rownames(enrichments_per_pwm[enrichments_per_pwm$`0-0.2lasso`==0,])
DIANE::get_gene_information(tfs_rf_pwm_bad, organism = "Arabidopsis thaliana")
# DIANE::get_gene_information(tfs_lasso_pwm_bad, organism = "Arabidopsis thaliana")
tfs_rf_pwm_pretty_bad <- rownames(enrichments_per_pwm[enrichments_per_pwm$`no diffrf`==0,])
# tfs_lasso_pwm_bad <- rownames(enrichments_per_pwm[enrichments_per_pwm$`0-0.2lasso`==0,])
DIANE::get_gene_information(tfs_rf_pwm_pretty_bad, organism = "Arabidopsis thaliana")
load("results/100_permutations_bRF_edges.rdata")
# 10 first replicates
edges <- edges[names(edges)[as.numeric(str_split_fixed(names(edges), '_', 5)[,4])<=10]]
net <- edges$bRF_1_trueData_1_0.005
# nodes <-
get_nodes_relative_rank <- function(net, nodes){
return(setNames(rank(table(net$from))[nodes]/length(unique(net$from)),nodes))
}
positive_genes <- names(clusters_rf[clusters_rf==2])
edges_positives <- lapply(edges, function(net){net[net$to %in% positive_genes,]})
negative_genes <- names(clusters_rf[clusters_rf==1])
edges_negatives <- lapply(edges, function(net){net[net$to %in% negative_genes,]})
relative_rank <- lapply(edges, get_nodes_relative_rank, tfs_rf_pwm_pos)
relative_rank_pos <- lapply(edges_positives, get_nodes_relative_rank, tfs_rf_pwm_pos)
data.frame(relative_rank) %>%
rownames_to_column("gene") %>%
reshape2::melt() %>%
separate(variable, into = c("method", "alpha", "dataset", "rep", "density"), sep = '_', remove = F) %>%
mutate(alpha = as.numeric(alpha)) %>%
ggplot(aes(x=alpha, y=value, color = dataset)) +
ggh4x::facet_nested_wrap(vars(density, gene), nest_line = T) +
geom_point() + geom_smooth() +
theme(strip.background = element_blank(), axis.title.x = element_text(size = 22),
title = element_text(size = 16), strip.text = element_text(size = 16),
legend.text = element_text(size = 15), axis.text = element_text(size = 12),
legend.position = 'left') + ggtitle("TFs with motifs enriched in positive genes promoters : relative out-degree rank")
data.frame(relative_rank_pos) %>%
rownames_to_column("gene") %>%
reshape2::melt() %>%
separate(variable, into = c("method", "alpha", "dataset", "rep", "density"), sep = '_', remove = F) %>%
mutate(alpha = as.numeric(alpha)) %>%
ggplot(aes(x=alpha, y=value, color = dataset)) +
ggh4x::facet_nested_wrap(vars(density, gene), nest_line = T) +
geom_point() + geom_smooth() +
theme(strip.background = element_blank(), axis.title.x = element_text(size = 22),
title = element_text(size = 16), strip.text = element_text(size = 16),
legend.text = element_text(size = 15), axis.text = element_text(size = 12),
legend.position = 'left') + ggtitle("TFs with motifs enriched in positive genes promoters : relative out-degree rank in positive subset")
relative_rank_bad <- lapply(edges, get_nodes_relative_rank, tfs_rf_pwm_bad)
relative_rank_bad_subset <- lapply(edges_negatives, get_nodes_relative_rank, tfs_rf_pwm_bad)
data.frame(relative_rank_bad) %>%
rownames_to_column("gene") %>%
reshape2::melt() %>%
separate(variable, into = c("method", "alpha", "dataset", "rep", "density"), sep = '_', remove = F) %>%
mutate(alpha = as.numeric(alpha)) %>%
ggplot(aes(x=alpha, y=value, color = dataset)) +
ggh4x::facet_nested_wrap(vars(density, gene), nest_line = T) +
geom_point() + geom_smooth() +
theme(strip.background = element_blank(), axis.title.x = element_text(size = 22),
title = element_text(size = 16), strip.text = element_text(size = 16),
legend.text = element_text(size = 15), axis.text = element_text(size = 12),
legend.position = 'left') + ggtitle("TFs with motifs enriched in negative genes promoters : relative out-degree rank")
data.frame(relative_rank_bad_subset) %>%
rownames_to_column("gene") %>%
reshape2::melt() %>%
separate(variable, into = c("method", "alpha", "dataset", "rep", "density"), sep = '_', remove = F) %>%
mutate(alpha = as.numeric(alpha)) %>%
ggplot(aes(x=alpha, y=value, color = dataset)) +
ggh4x::facet_nested_wrap(vars(density, gene), nest_line = T) +
geom_point() + geom_smooth() +
theme(strip.background = element_blank(), axis.title.x = element_text(size = 22),
title = element_text(size = 16), strip.text = element_text(size = 16),
legend.text = element_text(size = 15), axis.text = element_text(size = 12),
legend.position = 'left') + ggtitle("TFs with motifs enriched in negative genes promoters : relative out-degree rank in negative subset")
library(DIANE)
background <- convert_from_agi(genes)
for(group in unique(clusters_rf)){
# genes_i <- names(which(clusters_lasso == group))
#
# print(paste("LASSO", length(genes_i), "genes,", group, "\n"))
# genes_i <- convert_from_agi(genes_i)
# go_lasso <- enrich_go(genes_i, background)
# DIANE::draw_enrich_go(go_lasso)
# go_lasso
genes_i <- names(which(clusters_rf == group))
print(paste("RF", length(genes_i), "genes, group", group))
genes_i <- convert_from_agi(genes_i)
go_rf <- enrich_go(genes_i, background)
DIANE::draw_enrich_go(go_rf)
print(go_rf)
}
expression <- data.frame(counts)
expression <- (expression-rowMeans(expression)) / matrixStats::rowSds(as.matrix(expression))
Heatmap(expression, cluster_columns = F, show_row_names = F)+
rowAnnotation(
clusters_rf = clusters_rf[rownames(expression)],
col=list(clusters_rf= setNames(c("darkorange", "darkgreen", "lightgrey"),
nm = names(table(clusters_rf)))))